Large-scale international collaborations such as ATLAS rely on globally distributed workflows and data management to process, move, and store vast volumes of data. ATLAS's Production and Distributed Analysis (PanDA) workflow system and the Rucio data management system are each highly optimized for their respective design goals. However, operating them together at global scale exposes systemic inefficiencies, including underutilized resources, redundant or unnecessary transfers, and altered error distributions. Moreover, PanDA and Rucio currently lack shared performance awareness and coordinated, adaptive strategies. This work charts a path toward co-optimizing the two systems by diagnosing data-management pitfalls and prioritizing end-to-end improvements. With the observation of spatially and temporally imbalanced transfer activities, we develop a metadata-matching algorithm that links PanDA jobs and Rucio datasets at the file level, yielding a complete, fine-grained view of data access and movement. Using this linkage, we identify anomalous transfer patterns that violate PanDA's data-centric job-allocation principle. We then outline mitigation strategies for these patterns and highlight opportunities for tighter PanDA-Rucio coordination to improve resource utilization, reduce unnecessary data movement, and enhance overall system resilience.

翻译：诸如ATLAS这样的大规模国际合作项目依赖全球分布式工作流与数据管理系统来处理、传输和存储海量数据。ATLAS的生产与分布式分析（PanDA）工作流系统与Rucio数据管理系统各自针对其设计目标进行了高度优化。然而，在全球范围内协同运行这两个系统时，会暴露出系统性效率低下的问题，包括资源利用率不足、冗余或非必要的数据传输，以及异常的错误分布模式。此外，PanDA与Rucio目前缺乏共享的性能感知机制以及协调、自适应的策略。本研究通过诊断数据管理中的缺陷并优先考虑端到端的改进，为两个系统的协同优化规划了路径。通过观察时空分布不均衡的传输活动，我们开发了一种元数据匹配算法，该算法能在文件级别将PanDA作业与Rucio数据集关联起来，从而提供关于数据访问与移动的完整、细粒度视图。利用这种关联，我们识别了违反PanDA以数据为中心的作业分配原则的异常传输模式。随后，我们针对这些模式提出了缓解策略，并强调了加强PanDA与Rucio协调的机遇，以提高资源利用率、减少不必要的数据移动，并增强整体系统的鲁棒性。